PyTorch implementation of normalizing flow models
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Updated
Jul 9, 2024 - Python
PyTorch implementation of normalizing flow models
Official PyTorch implementation of "HiNet: Deep Image Hiding by Invertible Network" (ICCV 2021)
Constrained optimization toolkit for PyTorch
A Julia framework for invertible neural networks
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.
Multi-fidelity Generative Deep Learning Turbulent Flows
MintNet: Building Invertible Neural Networks with Masked Convolutions
A python/pytorch package for invertible neural networks
[NeurIPS 2022] (Amortized) distributional control for pre-trained generative models
Learning inverse kinematics using invertible neural networks and GANs. Research project for "Advanced Deep Learning for Robotics".
Results of my master thesis. Conditional invertible neural networks in the freia framework were used to dertermine the CO2 concentration using spectra taken by the satellite OCO2.
Research project for real-time rendering using Neural Radiance Fields (NeRF) and invertible neural networks (INNs)
Repository for "Inverse Kinematics of Tendon Driven Continuum Robots using Invertible Neural Network" (CompAuto 2022)
Code to reproduce results in "Preconditioned training of normalizing flows for variational inference in inverse problems"
This contains my pytorch implementation of Glow from OpenAI.
RID-Noise: Towards Robust Inverse Design under Noisy Environments
Project website for 'Estimating "good" variability in speech production using invertible neural networks' (ISSP 2020)
Null-sampling for Interpretable and Fair Representations
FrEIA sample code
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